DocumentCode
1636304
Title
Multiobjective optimization using dynamic neighborhood particle swarm optimization
Author
Hu, Xiaohui ; Eberhart, Russell
Author_Institution
Dept. of Biomed. Eng., Purdue Univ., West Lafayette, IN, USA
Volume
2
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
1677
Lastpage
1681
Abstract
This paper presents a particle swarm optimization (PSO) algorithm for multiobjective optimization problems. PSO is modified by using a dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. Several benchmark cases were tested and showed that PSO could efficiently find multiple Pareto optimal solutions
Keywords
Pareto distribution; evolutionary computation; optimisation; dynamic neighborhood particle swarm optimization; multiobjective optimization; multiple Pareto optimal solutions; one-dimension optimization; particle memory updating; Benchmark testing; Biomedical computing; Biomedical engineering; Design optimization; Equations; Evolutionary computation; Genetic algorithms; Pareto optimization; Particle swarm optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
Conference_Location
Honolulu, HI
Print_ISBN
0-7803-7282-4
Type
conf
DOI
10.1109/CEC.2002.1004494
Filename
1004494
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